DNA Similarity Search With Access Control Over Encrypted Cloud Data
Bibliographic record
Abstract
DNA similarity search has been widely applied in human genomic studies including DNA marking, genomic sequencing and genetic disease prediction. Meanwhile, with the explosive growth of data, users are increasingly inclining to store DNA data on the cloud for saving local cost. However, the high sensitivity of DNA data has forced the government to strictly control its acquisition and utilization. One potential solution is to encrypt DNA data before outsourcing them to the cloud. Nevertheless, private DNA similarity query has been an active research issue, state-of-the-art results are still defective in security, functionality, and efficiency. In this article, we propose EFSS, an efficient and fine-grained similarity search scheme over encrypted DNA data. In specific, first, we design an approximation algorithm to efficiently calculate the edit distances between two sequences. Second, we put forward a novel Boolean search strategy to achieve complicated logic queries such as mixed “AND” and “NO” operations on genes. Third, data access control is also supported in our EFSS through a variant of polynomial based design. Moreover, the K-means clustering algorithm is exploited to further improve the efficiency of execution. In the end, security analysis and extensive experiments demonstrate the high performance of EFSS compared with existing schemes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".